Abstract
BackgroundThe immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment.MethodsTo accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm.ResultsA total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively.ConclusionsThe FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.
Highlights
The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases
A toxicity equivalence factor (TEF) method proposed by U.S Environmental Protection Agency (EPA) is applied to estimate the overall toxicity posed by polycyclic aromatic hydrocarbons (PAHs) mixtures
Identification of informative features According to our previous report [14], several experiments utilizing various combinations of features have been conducted to show that both chemical and biological features are required for constructing predictive model
Summary
The immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. Methods: To accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proin‐ flammatory potentials of engine exhausts. The informative features were utilized to develop a com‐ putational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. Among the chemicals from engine exhausts, polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs are substances of major concern due to their known genotoxicity effects [6] and are suspected carcinogens in humans [7]. A TEQ value representing the overall toxicity of a PAH mixture is calculated based on the summation of equipotent concentrations of BaP converted from PAHs. The TEF/TEQ method has been widely applied to calculate the carcinogenicity and mutagenicity of PAH mixtures in environmental samples [8,9,10,11,12,13]. As a complement to TEF method, Ames test [15], which is capable of determining the genotoxicity of engine exhausts, is routinely conducted to provide an overall genotoxicity of mixtures [16, 17]
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